An Empirical Study on the Bugs Found while Reusing Pre-trained Natural Language Processing Models

Understand

In NLP, reusing pre-trained models instead of training from scratch has gained popularity; however, NLP models are mostly black boxes, very large, and often require significant resources.

• To ease, models trained with large corpora are made available, and developers reuse them for different problems.
• In contrast, developers mostly build their models from scratch for traditional DL-related problems.
• By doing so, they have control over the choice of algorithms, data processing, model structure, tuning hyperparameters, etc.